Job Title: Senior (ML Ops) arun.raja@dhira.ai

Job Title: Senior Android Engineer (ML Ops) arun.raja@dhira.ai

Job Title: Senior Android Engineer (ML Ops)

Type:

Position Summary

Senior Android Engineer (ML Ops)

Key Responsibilities

ML Infrastructure & MLOps

• Design, implement, and manage scalable ML infrastructure to support diverse projects.
  
• Develop and maintain MLOps pipelines for continuous integration, delivery, and monitoring of ML models.
  
• Track and optimize model performance, implementing strategies for real-time improvements and scalability.
  
• Ensure robust version control, reproducibility, and governance for both ML models and datasets.
  

Android ML Deployment

• Implement model serving and asset management techniques (e.g.,

  Android Asset Packs

  ) to deploy

  ResNet

  and

  dlib

  -based models efficiently within Android applications.
• Collaborate with Android developers to integrate ML models into production apps with optimal inference performance.
  
• Develop and implement edge deployment strategies for achieving low-latency, high-accuracy performance on Android devices.
  

Collaboration & Stakeholder Engagement

• Work closely with data scientists, ML engineers, mobile developers, and product teams to align ML solutions with project objectives.
  
• Engage with stakeholders to gather requirements, provide technical recommendations, and deliver impactful ML-driven features.
  
• Contribute to internal knowledge-sharing, code reviews, and best practice improvements within the ML engineering team.
  

Required Qualifications

• Education:

  Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field.

• Experience:

  5–10 years of experience in developing and deploying machine learning models in production, including Android-based applications.

Technical Skills:

• Proficiency in

  Python

  ,

  Java

  , and ML libraries such as

  TensorFlow

  ,

  PyTorch

  ,

  OpenCV

  , and

  scikit-learn

  .
• Experience with MLOps tools, CI/CD platforms, and frameworks for model deployment, monitoring, and lifecycle management.
  
• Strong understanding of

  cloud services (AWS, Azure, GCP)

  , containerization (

  Docker, Kubernetes

  ), and mobile app integration workflows.
• Familiarity with

  feature store frameworks

  ,

  data versioning tools

  , and

  model asset management strategies

  .
• Hands-on experience deploying ML models on Android, with proven skills in optimizing for device constraints and performance.
  

Preferred Qualifications

• Experience with

  edge ML deployment techniques

  and tools.
• Familiarity with

  data privacy

  , security, and compliance frameworks for AI deployments.
• Excellent problem-solving, debugging, and performance tuning skills.
  
• Strong verbal and written communication, with the ability to explain complex ML concepts to diverse audiences.
  
• Demonstrated ability to work both independently and collaboratively in fast-paced, outcome-driven environments.